Skip to main content
SpringerLink
Log in

An Unsupervised Change Detection Approach for Remote Sensing Image Using SURF and SVM

  • Conference paper
  • First Online:
Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

Included in the following conference series:

Abstract

In this paper, we propose a novel approach for unsupervised change detection by integrating Speeded Up Robust Features (SURF) key points and Support Vector Machine (SVM) classifier. The approach starts by extracting SURF key points from both images and matches them using RANdom SAmple Consensus (RANSAC) algorithm. The matched key points are then viewed as training samples for unchanged class; on the other hand, those for changed class are selected from the remaining SURF key points based on Gaussian mixture model (GMM). Subsequently, training samples are utilized for training a SVM classifier. Finally, the classifier is used to segment the difference image into changed and unchanged classes. To demonstrate the effect of our approach, we compare it with the other four state-of-the-art change detection methods over three datasets, meanwhile extensive quantitative and qualitative analysis of the change detection results confirms the effectiveness of the proposed approach, showing its capability to consistently produce promising results on all the datasets without any priori assumptions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lu, D., Mausel, P., Brondizio, E., Moran, E.: Change detection techniques. Int. J. Rem. Sens. 25, 2365–2401 (2004)

    Article  Google Scholar 

  2. Melgani, F., Bazi, Y.: Markovian fusion approach to robust unsupervised change detection in remotely sensed imagery. IEEE Trans. Geosci. Rem. Sens. Lett. 3, 457–461 (2006)

    Article  Google Scholar 

  3. Bazi, Y., Melgani, F., Al-Sharari, D.: Unsupervised change detection in multispectral remotely sensed imagery with level set methods. IEEE Trans. Geosci. Rem. Sens. 48, 3178–3187 (2010)

    Article  Google Scholar 

  4. Rosin, D.L.: Thresholding for change detection. Comput. Vision Image Underst. 86, 79–95 (2002)

    Article  MATH  Google Scholar 

  5. Prakash, A., Gupta, R.P.: TLand-use mapping and change detection in a coal mining area - a case study in the Jharia coalfield, India. Int. J. Rem. Sens. 19, 391–410 (1998)

    Article  Google Scholar 

  6. Weismiller, R.A., Kristof, S.J., Scholz, D.K., Anuta, P.E., Momin, S.A.: Change detection in coastal zone environments. Photogram. Eng. Rem. Sens. 43, 1533–1539 (1977)

    Google Scholar 

  7. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vision Graph. Image Process 29, 273–285 (1985)

    Article  Google Scholar 

  8. Nelson, R.F.: Detecting forest canopy change due to insect activity using Landsat MSS. Photogram. Eng. Rem. Sens. 49, 1303–1314 (1983)

    Google Scholar 

  9. Stow, D.A.: Reducing the effects of misregistration on pixel-level change detection. Int. J. Rem. Sens. 20, 2477–2483 (1999)

    Article  Google Scholar 

  10. Jha, C.S., Unni, N.V.M.: Digital change detection of forest conversion of a dry tropical Indian forest region. Int. J. Rem. Sens. 15, 2543–2552 (2007)

    Article  Google Scholar 

  11. Sohl, T.L.: Change analysis in the United Arab Emirates: an investigation of techniques. Photogram. Eng. Rem. Sens. 65, 475–484 (1999)

    Google Scholar 

  12. Nackaerts, K., Vaesen, K., Muys, B., Coppin, P.: Comparative performance of a modified change vector analysis in forest change detection. Int. J. Rem. Sens. 26, 839–852 (2005)

    Article  Google Scholar 

  13. Fung, T., LeDrew, E.: The determination of optimal threshold levels for change detection using various accuracy indices. Photogram. Eng. Rem. Sens. 54, 1449–1454 (1988)

    Google Scholar 

  14. Bruzzone, L., Prieto, D.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Rem. Sens. 38, 1171–1182 (2000)

    Article  Google Scholar 

  15. Kasetkasem, T., Varshney, P.: An image change detection algorithm based on Markov random field models. IEEE Trans. Geosci. Rem. Sens. 40, 1815–1823 (2002)

    Article  Google Scholar 

  16. Celik, T.: Multiscale change detection in multitemporal satellite images. IEEE Trans. Geosci. Rem. Sens. Lett. 6, 820–824 (2009)

    Article  Google Scholar 

  17. Ghosh, A., Mishra, N.S., Ghosh, S.: Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf. Sci. 181, 699–715 (2011)

    Article  Google Scholar 

  18. Hao, M., Shi, W.Z., Zhang, H., Li, C.: Unsupervised change detection with expectation-maximization-based level set. IEEE Trans. Geosci. Rem. Sens. Lett. 11, 210–214 (2014)

    Article  Google Scholar 

  19. Kusetogullari, H., Yavariabdi, A., Celik, T.: Unsupervised change detection in multitemporal multispectral satellite images using parallel particle swarm optimization. IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens. 8, 2151–2164 (2015)

    Article  Google Scholar 

  20. Teke, M., Temizel, A.: Multi-spectral satellite image registration using scale-restricted SURF. In: 20th International Conference on Pattern Recognition, pp. 2310–2313 (2010)

    Google Scholar 

  21. Liang, J., Liu, Q., Ai, Q.S.: Research of image classification based on fusion of SURF and global feature. Comput. Eng. Appl. 49, 174–177 (2013)

    Google Scholar 

  22. Herbert, B., Andreas, E., Tuytelaars, T., Gool, L.V.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110, 346–359 (2008)

    Article  Google Scholar 

  23. Niranjan, M.: Support vector machines: a tutorial overview and critical appraisal. In: IEE Colloquium on Applied Statistical Pattern Recognition, p. 2/1 (1999)

    Google Scholar 

  24. Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10, 988–999 (1999)

    Article  Google Scholar 

  25. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  26. Bilmes, J.A.: A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Int. Comput. Sci. Inst. 4, 1–13 (1998)

    Google Scholar 

  27. Chang, C.C., Lin, C.J.: LIBSVM–A Library for Support Vector Machines. http://www.csie.ntu.edu.tw//~cjlin/libsvm/index.html

  28. University of Maryland: Global Land Cover Facility. http://www.landcover.org/

Download references

Author information

Authors and Affiliations

  1. Chongqing Communication College, Chongqing, 400035, China

    Lin Wu & Jiangtao Long

  2. Beihang University, Beijing, 100191, China

    Yunhong Wang

Authors
  1. Lin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunhong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiangtao Long
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Wu .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Wu, L., Wang, Y., Long, J. (2016). An Unsupervised Change Detection Approach for Remote Sensing Image Using SURF and SVM. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_44

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3005-5_44

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation